Statistical identification with hidden Markov models of large order splitting strategies in an equity market

Large trades in a financial market are usually split into smaller parts and traded incrementally over extended periods of time. We address these large trades as hidden orders. In order to identify and characterize hidden orders we fit hidden Markov models to the time series of the sign of the tick by tick inventory variation of market members of the Spanish Stock Exchange. Our methodology probabilistically detects trading sequences, which are characterized by a net majority of buy or sell transactions. We interpret these patches of sequential buying or selling transactions as proxies of the traded hidden orders. We find that the time, volume and number of transactions size distributions of these patches are fat tailed. Long patches are characterized by a high fraction of market orders and a low participation rate, while short patches have a large fraction of limit orders and a high participation rate. We observe the existence of a buy-sell asymmetry in the number, average length, average fraction of market orders and average participation rate of the detected patches. The detected asymmetry is clearly depending on the local market trend. We also compare the hidden Markov models patches with those obtained with the segmentation method used in Vaglica {\it et al.} (2008) and we conclude that the former ones can be interpreted as a partition of the latter ones.Comment: 26 pages, 12 figure

Trading activity and price impact in parallel markets: SETS vs. off-book market at the London Stock Exchange

We empirically study the trading activity in the electronic on-book segment and in the dealership off-book segment of the London Stock Exchange, investigating separately the trading of active market members and of other market participants which are non-members. We find that (i) the volume distribution of off-book transactions has a significantly fatter tail than the one of on-book transactions, (ii) groups of members and non-members can be classified in categories according to their trading profile (iii) there is a strong anticorrelation between the daily inventory variation of a market member due to the on-book market transactions and inventory variation due to the off-book market transactions with non-members, and (iv) the autocorrelation of the sign of the orders of non-members in the off-book market is slowly decaying. We also analyze the on-book price impact function over time, both for positive and negative lags, of the electronic trades and of the off-book trades. The unconditional impact curves are very different for the electronic trades and the off-book trades. Moreover there is a small dependence of impact on the volume for the on-book electronic trades, while the shape and magnitude of impact function of off-book transactions strongly depend on volume.Comment: 16 pages, 9 figure

Scaling laws of strategic behaviour and size heterogeneity in agent dynamics

The dynamics of many socioeconomic systems is determined by the decision making process of agents. The decision process depends on agent's characteristics, such as preferences, risk aversion, behavioral biases, etc.. In addition, in some systems the size of agents can be highly heterogeneous leading to very different impacts of agents on the system dynamics. The large size of some agents poses challenging problems to agents who want to control their impact, either by forcing the system in a given direction or by hiding their intentionality. Here we consider the financial market as a model system, and we study empirically how agents strategically adjust the properties of large orders in order to meet their preference and minimize their impact. We quantify this strategic behavior by detecting scaling relations of allometric nature between the variables characterizing the trading activity of different institutions. We observe power law distributions in the investment time horizon, in the number of transactions needed to execute a large order and in the traded value exchanged by large institutions and we show that heterogeneity of agents is a key ingredient for the emergence of some aggregate properties characterizing this complex system.Comment: 6 pages, 3 figure

Specialization of strategies and herding behavior of trading firms in a financial market

The understanding of complex social or economic systems is an important scientific challenge. Here we present a comprehensive study of the Spanish Stock Exchange showing that most financial firms trading in that market are characterized by a resulting strategy and can be classified in groups of firms with different specialization. Few large firms overally act as trending firms whereas many heterogeneous firm act as reversing firms. The herding properties of these two groups are markedly different and consistently observed over a four-year period of trading.Comment: 8 pages, 5 figure

Market Impact and Trading Protocols of Hidden Orders in Stock Markets

We empirically study the market impact of trading orders. We are specically interested in large trading orders that are executed incrementally, which we call hidden orders. These are reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We nd that market impact is strongly concave, approximately increasing as the square root of order size. Furthermore, as a given order is executed, the impact grows in time according to a power-law; after the order is nished, it reverts to a level of about 0:5We empirically study the market impact of trading orders. We are specically interested in large trading orders that are executed incrementally, which we call hidden orders. These are reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We nd that market impact is strongly concave, approximately increasing as the square root of order size. Furthermore, as a given order is executed, the impact grows in time according to a power-law; after the order is nished, it reverts to a level of about 0:5Refereed Working Papers / of international relevanc

Market impact and trading profile of large trading orders in stock markets

We empirically study the market impact of trading orders. We are specifically interested in large trading orders that are executed incrementally, which we call hidden orders. These are reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We find that market impact is strongly concave, approximately increasing as the square root of order size. Furthermore, as a given order is executed, the impact grows in time according to a power-law; after the order is finished, it reverts to a level of about 0.5-0.7 of its value at its peak. We observe that hidden orders are executed at a rate that more or less matches trading in the overall market, except for small deviations at the beginning and end of the order.Comment: 9 pages, 7 figure

An In Vitro Model of Glioma Development

Gliomas are the prevalent forms of brain cancer and derive from glial cells. Among them, astrocytomas are the most frequent. Astrocytes are fundamental for most brain functions, as they contribute to neuronal metabolism and neurotransmission. When they acquire cancer properties, their functions are altered, and, in addition, they start invading the brain parenchyma. Thus, a better knowledge of transformed astrocyte molecular properties is essential. With this aim, we previously developed rat astrocyte clones with increasing cancer properties. In this study, we used proteomic analysis to compare the most transformed clone (A-FC6) with normal primary astrocytes. We found that 154 proteins are downregulated and 101 upregulated in the clone. Moreover, 46 proteins are only expressed in the clone and 82 only in the normal cells. Notably, only 11 upregulated/unique proteins are encoded in the duplicated q arm of isochromosome 8 (i(8q)), which cytogenetically characterizes the clone. Since both normal and transformed brain cells release extracellular vesicles (EVs), which might induce epigenetic modifications in the neighboring cells, we also compared EVs released from transformed and normal astrocytes. Interestingly, we found that the clone releases EVs containing proteins, such as matrix metalloproteinase 3 (MMP3), that can modify the extracellular matrix, thus allowing invasion

Statistical identification with hidden Markov models of large order splitting strategies in an equity market

Large trades in a financial market are usually split into smaller parts and traded incrementally over extended periods of time. We address these large trades as hidden orders. In order to identify and characterize hidden orders we fit hidden Markov models to the time series of the sign of the tick by tick inventory variation of market members of the Spanish Stock Exchange. Our methodology probabilistically detects trading sequences, which are characterized by a net majority of buy or sell transactions. We interpret these patches of sequential buying or selling transactions as proxies of the traded hidden orders. We find that the time, volume and number of transactions size distributions of these patches are fat tailed. Long patches are characterized by a high fraction of market orders and a low participation rate, while short patches have a large fraction of limit orders and a high participation rate. We observe the existence of a buy-sell asymmetry in the number, average length, average fraction of market orders and average participation rate of the detected patches. The detected asymmetry is clearly depending on the local market trend. We also compare the hidden Markov models patches with those obtained with the segmentation method used in Vaglica {\it et al.} (2008) and we conclude that the former ones can be interpreted as a partition of the latter ones.